Update GraphSynthesizer.

gklearn/dataset/__init__.py

@@ -16,4 +16,6 @@ __date__ = "October 2020"
 from gklearn.dataset.metadata import DATABASES, DATASET_META
 from gklearn.dataset.metadata import GREYC_META, IAM_META, TUDataset_META
 from gklearn.dataset.metadata import list_of_databases, list_of_datasets
-from gklearn.dataset.data_fetcher import DataFetcher
+from gklearn.dataset.data_fetcher import DataFetcher
+from gklearn.dataset.graph_synthesizer import GraphSynthesizer
+from gklearn.dataset.dataset import Dataset, split_dataset_by_target

gklearn/dataset/graph_synthesizer.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Sep 11 18:10:06 2020
+
+@author: ljia
+"""
+import numpy as np
+import networkx as nx
+import random
+
+
+class GraphSynthesizer(object):
+	
+	
+	def __init__(self, g_type=None, *args, **kwargs):
+		if g_type == 'unified':
+			self._graphs = self.unified_graphs(*args, *kwargs)
+		else:
+			self._graphs = None
+	
+	
+	def random_graph(self, num_nodes, num_edges, num_node_labels=0, num_edge_labels=0, seed=None, directed=False, max_num_edges=None, all_edges=None):
+		g = nx.Graph()
+		if num_node_labels > 0:
+			node_labels = np.random.randint(0, high=num_node_labels, size=num_nodes)
+			for i in range(0, num_nodes):
+				g.add_node(str(i), atom=node_labels[i]) # @todo: update "atom".
+		else:
+			for i in range(0, num_nodes):
+				g.add_node(str(i))
+
+		if num_edge_labels > 0:
+			edge_labels = np.random.randint(0, high=num_edge_labels, size=num_edges)				
+			for idx, i in enumerate(random.sample(range(0, max_num_edges), num_edges)):
+				node1, node2 = all_edges[i]
+				g.add_edge(str(node1), str(node2), bond_type=edge_labels[idx])  # @todo: update "bond_type".
+		else:
+			for i in random.sample(range(0, max_num_edges), num_edges):
+				node1, node2 = all_edges[i]
+				g.add_edge(str(node1), str(node2))
+		
+		return g
+	
+	
+	def unified_graphs(self, num_graphs=1000, num_nodes=20, num_edges=40, num_node_labels=0, num_edge_labels=0, seed=None, directed=False):
+		max_num_edges = int((num_nodes - 1) * num_nodes / 2)
+		if num_edges > max_num_edges:
+			raise Exception('Too many edges.')
+		all_edges = [(i, j) for i in range(0, num_nodes) for j in range(i + 1, num_nodes)] # @todo: optimize. No directed graphs.
+			
+		graphs = []
+		for idx in range(0, num_graphs):		
+			graphs.append(self.random_graph(num_nodes, num_edges, num_node_labels=num_node_labels, num_edge_labels=num_edge_labels, seed=seed, directed=directed, max_num_edges=max_num_edges, all_edges=all_edges))
+			
+		return graphs
+	
+	
+	@property
+	def graphs(self):
+		return self._graphs

gklearn/utils/graph_synthesizer.py

@@ -13,6 +13,11 @@ import random
 class GraphSynthesizer(object):
 	
 	
+	import warnings
+	warnings.simplefilter('always', DeprecationWarning)
+	warnings.warn('This class has been moved to "gklearn.dataset" module. The class "gklearn.utils.graph_synthesizer.GraphSynthesizer" has not been maintained since Nov 12th, 2020 (version 0.2.1) and will be removed since version 0.2.2.', DeprecationWarning)
+	
+	
 	def __init__(self):
 		pass